OpenRaiser/Intern-Atlas huggingface dataset:
Boardwalk: Towards a Framework for Creating Board Games with LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Implementing board games in code can be a time-consuming task. However, Large Language Models (LLMs) have been proven effective at generating code for domain-specific tasks with simple contextual information. We aim to investigate whether LLMs can implement digital versions of board games from rules described in natural language. This would be a step towards an LLM-assisted framework for quick board game code generation. We expect to determine the main challenges for LLMs to implement the board games, and how different approaches and models compare to one another. We task three state-of-the-art LLMs (Claude, DeepSeek and ChatGPT) with coding a selection of 12 popular and obscure games in free-form and within Boardwalk, our proposed General Game Playing API. We anonymize the games and components to avoid evoking pre-trained LLM knowledge. The implementations are tested for playability and rule compliance. We evaluate success rate and common errors across LLMs and game popularity. Our approach proves viable, with the best performing model, Claude 3.7 Sonnet, yielding 55.6\% of games without any errors. While compliance with the API increases error frequency, the severity of errors is more significantly dependent on the LLM. We outline future steps for creating a framework to integrate this process, making the elaboration of board games more accessible.
Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media
Venue: arXiv cs.SI 2025
Year: 2025

Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content varies by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.
Modular Embedding Recomposition for Incremental Learning
Venue: arXiv cs.AI 2025
Year: 2025

The advent of pre-trained Vision-Language Models (VLMs) has significantly transformed Continual Learning (CL), mainly due to their zero-shot classification abilities. Such proficiency makes VLMs well-suited for real-world applications, enabling robust performance on novel unseen classes without requiring adaptation. However, fine-tuning remains essential when downstream tasks deviate significantly from the pre-training domain. Prior CL approaches primarily focus on preserving the zero-shot capabilities of VLMs during incremental fine-tuning on a downstream task. We take a step further by devising an approach that transforms preservation into enhancement of the zero-shot capabilities of VLMs. Our approach, named MoDular Embedding Recomposition (MoDER), introduces a modular framework that trains multiple textual experts, each specialized in a single seen class, and stores them in a foundational hub. At inference time, for each unseen class, we query the hub and compose the retrieved experts to synthesize a refined prototype that improves classification. We show the effectiveness of our method across two popular zero-shot incremental protocols, Class-IL and MTIL, comprising a total of 14 datasets. The codebase is available at https://github.com/aimagelab/mammoth.
SafeSpace: An Integrated Web Application for Digital Safety and Emotional Well-being
Venue: arXiv cs.HC 2025
Year: 2025

In the digital era, individuals are increasingly exposed to online harms such as toxicity, manipulation, and grooming, which often pose emotional and safety risks. Existing systems for detecting abusive content or issuing safety alerts operate in isolation and rarely combine digital safety with emotional well-being. In this paper, we present SafeSpace, a unified web application that integrates three modules: (1) toxicity detection in chats and screenshots using NLP models and Google's Perspective API, (2) a configurable safety ping system that issues emergency alerts with the user's live location (longitude and latitude) via SMTP-based emails when check-ins are missed or SOS alerts are manually triggered, and (3) a reflective questionnaire that evaluates relationship health and emotional resilience. The system employs Firebase for alert management and a modular architecture designed for usability, privacy, and scalability. The experimental evaluation shows 93% precision in toxicity detection, 100% reliability in safety alerts under emulator tests, and 92% alignment between automated and manual questionnaire scoring. SafeSpace, implemented as a web application, demonstrates the feasibility of integrating detection, protection, and reflection within a single platform, with future deployment envisioned as a mobile application for broader accessibility.
FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline
Venue: arXiv cs.LG 2025
Year: 2025

Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic data pipeline, such as the impact of filtering low-quality problems. To address this gap, we introduce FLAMES, a Framework for LLM Assessment of Math rEasoning Data Synthesis, and perform a systematic study of 10 existing data synthesis strategies and multiple other factors impacting the performance of synthetic math reasoning data. Our FLAMES experiments provide several valuable insights about the optimal balance of difficulty and diversity of synthetic data. First, data agents designed to increase problem complexity lead to best improvements on most math metrics. Second, with a fixed data generation budget, keeping higher problem coverage is more important than keeping only problems with reliable solutions. Third, GSM8K- and MATH-based synthetic data can lead to improvements on competition-level benchmarks, showcasing easy-to-hard generalization. Leveraging insights from our FLAMES experiments, we design two novel data synthesis strategies for improving out-of-domain generalization and robustness. Further, we develop the FLAMES dataset, an effective blend of our novel and existing data synthesis strategies, outperforming public datasets on OlympiadBench (+15.7), CollegeMath (+4.5), GSMPlus (+6.5), and MATH (+3.1). Fine-tuning Qwen2.5-Math-7B on the FLAMES dataset achieves 81.4% on MATH, surpassing larger Llama3 405B, GPT-4o and Claude 3.5 Sonnet.
The Community Index: A More Comprehensive Approach to Assessing Scholarly Impact
Venue: arXiv cs.DL 2025
Year: 2025

The h index is a widely recognized metric for assessing the research impact of scholars, defined as the maximum value h such that the scholar has published h papers each cited at least h times. While it has proven useful measuring individual scholarly productivity and citation impact, the h index has limitations, such as an inability to account for interdisciplinary collaboration or demographic differences in citation patterns. Moreover, it is sometimes mistakenly treated as a measure of research quality, even though it only reflects how often work has been cited. While metric based evaluations of research have grown in importance in some areas of academia, such as medicine, these evaluations fail to consider other important aspects of intellectual work, such as representational and epistemic diversity in research. In this article, we propose a new metric called the c index, or the community index, which combines multiple dimensions of scholarly impact. This is important because a plurality of perspectives and lived experiences within author teams can promote epistemological reflection and humility as part of the creation and validation of scientific knowledge. The c index is a means of accounting for the often global, and increasingly interdisciplinary nature of contemporary research, in particular, the data that is collected, curated and analyzed in the process of scientific inquiry. While the c index provides a means of quantifying diversity within research teams, diversity is integral to the advancement of scientific excellence and should be actively fostered through formal recognition and valuation. We herein describe the mathematical foundation of the c index and demonstrate its potential to provide a more comprehensive representation and more multidimensional assessment of scientific contributions of research impact as compared to the h index.
Real-time 3D Light-field Viewing with Eye-tracking on Conventional Displays
Venue: arXiv cs.GR 2025
Year: 2025

Creating immersive 3D visual experiences typically requires expensive and specialized hardware such as VR headsets, autostereoscopic displays, or active shutter glasses. These constraints limit the accessibility and everyday use of 3D visualization technologies in resource-constrained settings. To address this, we propose a low-cost system that enables real-time 3D light-field viewing using only a standard 2D monitor, a conventional RGB webcam, and red-cyan anaglyph glasses. The system integrates real-time eye-tracking to dynamically adapt the displayed light-field image to the user's head position with a lightweight rendering pipeline that selects and composites stereoscopic views from pre-captured light-field data. The resulting anaglyph image is updated in real-time, creating a more immersive and responsive 3D experience. The system operates entirely on CPU and maintains a stable frame rate of 30 FPS, confirming its feasibility on typical consumer-grade hardware. All of these highlight the potential of our approach as an accessible platform for interactive 3D applications in education, digital media, and beyond.
Enhanced NIRMAL Optimizer With Damped Nesterov Acceleration: A Comparative Analysis
Venue: arXiv cs.IR 2025
Year: 2025

This study introduces the Enhanced NIRMAL (Novel Integrated Robust Multi-Adaptation Learning with Damped Nesterov Acceleration) optimizer, an improved version of the original NIRMAL optimizer. By incorporating an $(α, r)$-damped Nesterov acceleration mechanism, Enhanced NIRMAL improves convergence stability while retaining chess-inspired strategies of gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We evaluate Enhanced NIRMAL against Adam, SGD with Momentum, Nesterov, and the original NIRMAL on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, using tailored convolutional neural network (CNN) architectures. Enhanced NIRMAL achieves a test accuracy of 46.06\% and the lowest test loss (1.960435) on CIFAR-100, surpassing the original NIRMAL (44.34\% accuracy) and closely rivaling SGD with Momentum (46.43\% accuracy). These results underscore Enhanced NIRMAL's superior generalization and stability, particularly on complex datasets.
Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Venue: arXiv cs.LG 2025
Year: 2025

Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly access private edge data, limiting their adaptation. Federated learning (FL) provides a privacy-aware alternative, but existing FL approaches overlook the constraints imposed by edge devices -- namely, limited computational resources and the scarcity of labeled data. To address these challenges, we introduce Practical Semi-Supervised Federated Learning (PSSFL), where edge devices hold only unlabeled, low-resolution data, while the server has limited labeled, high-resolution data. In this setting, we propose the Federated Mixture of Experts (FedMox), a novel framework that enhances FM adaptation in FL. FedMox tackles computational and resolution mismatch challenges via a sparse Mixture-of-Experts architecture, employing a spatial router to align features across resolutions and a Soft-Mixture strategy to stabilize semi-supervised learning. We take object detection as a case study, and experiments on real-world autonomous driving datasets demonstrate that FedMox effectively adapts FMs under PSSFL, significantly improving performance with constrained memory costs on edge devices. Our work paves the way for scalable and privacy-preserving FM adaptation in federated scenarios.
Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Venue: arXiv cs.LG 2025
Year: 2025

Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech is common, comparisons with flat-start training remain underexplored. We compare flat-start training across multiple datasets, SSL representations (WavLM, XEUS), and decoder architectures. Our results show that SSL representations are biased toward adult speech, with flat-start training on child speech mitigating these biases. We also analyze model scaling, finding consistent improvements up to 1B parameters, beyond which performance plateaus. Additionally, age-related ASR and speaker verification analysis highlights the limitations of proprietary models like Whisper, emphasizing the need for open-data models for reliable child speech research. All investigations are conducted using ESPnet, and our publicly available benchmark provides insights into training strategies for robust child speech processing.
The Rhythm of Tai Chi: Revitalizing Cultural Heritage in Virtual Reality through Interactive Visuals
Venue: arXiv cs.HC 2025
Year: 2025

The Rhythm of Tai Chi reinterprets the ancient Chinese martial art as a dynamic, interactive virtual reality (VR) experience. By leveraging computer vision and multimedia technologies, the project transforms Tai Chi's philosophy and movements into an immersive digital form. Real-time motion tracking captures user gestures, while visual feedback systems simulate the flow of Qi, enabling an intuitive and engaging practice environment. Beyond technological innovation, this work bridges traditional Chinese culture and modern audiences. It offers a global platform - accessible even to those unfamiliar with Tai Chi - to explore its cultural significance, connections to balance, health, and mindfulness. Serving as both a preservation tool and an educational resource, The Rhythm of Tai Chi revitalizes this heritage for the digital age.
Negative Shanshui: Real-time Interactive Ink Painting Synthesis
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents Negative Shanshui, a real-time interactive AI synthesis approach that reinterprets classical Chinese landscape ink painting, i.e., shanshui, to engage with ecological crises in the Anthropocene. Negative Shanshui optimizes a fine-tuned Stable Diffusion model for real-time inferences and integrates it with gaze-driven inpainting, frame interpolation; it enables dynamic morphing animations in response to the viewer's gaze and presents as an interactive virtual reality (VR) experience. The paper describes the complete technical pipeline, covering the system framework, optimization strategies, gaze-based interaction, and multimodal deployment in an art festival. Further analysis of audience feedback collected during its public exhibition highlights how participants variously engaged with the work through empathy, ambivalence, and critical reflection.
CrystalDiT: A Diffusion Transformer for Crystal Generation
Venue: arXiv cs.LG 2025
Year: 2025

We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.
Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection
Venue: arXiv cs.CR 2025
Year: 2025

AI-based solutions demonstrate remarkable results in identifying vulnerabilities in software, but research has consistently found that this performance does not generalize to unseen codebases. In this paper, we specifically investigate the impact of model architecture, parameter configuration, and quality of training data on the ability of these systems to generalize. For this purpose, we introduce VulGate, a high quality state of the art dataset that mitigates the shortcomings of prior datasets, by removing mislabeled and duplicate samples, updating new vulnerabilities, incorporating additional metadata, integrating hard samples, and including dedicated test sets. We undertake a series of experiments to demonstrate that improved dataset diversity and quality substantially enhances vulnerability detection. We also introduce and benchmark multiple encoder-only and decoder-only models. We find that encoder-based models outperform other models in terms of accuracy and generalization. Our model achieves \textbf{6.8\%} improvement in recall on the benchmark BigVul dataset and outperforms others on unseen projects, demonstrating enhanced generalizability. Our results highlight the role of data quality and model selection in the development of robust vulnerability detection systems. Our findings suggest a direction for future systems with high cross-project effectiveness.
Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) face a fundamental challenge in deciding when to rely on rapid, intuitive responses versus engaging in slower, more deliberate reasoning. Inspired by Daniel Kahneman's dual-process theory and his insights on human cognitive biases, we propose a novel Cognitive Decision Routing (CDR) framework that dynamically determines the appropriate reasoning strategy based on query characteristics. Our approach addresses the current limitations where models either apply uniform reasoning depth or rely on computationally expensive methods for all queries. We introduce a meta-cognitive layer that analyzes query complexity through multiple dimensions: correlation strength between given information and required conclusions, domain boundary crossings, stakeholder multiplicity, and uncertainty levels. Through extensive experiments on diverse reasoning tasks, we demonstrate that CDR achieves superior performance while reducing computational costs by 34\% compared to uniform deep reasoning approaches. Our framework shows particular strength in professional judgment tasks, achieving 23\% improvement in consistency and 18\% better accuracy on expert-level evaluations. This work bridges cognitive science principles with practical AI system design, offering a principled approach to adaptive reasoning in LLMs.
AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
Venue: arXiv cs.CY 2025
Year: 2025

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more detail. We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively.
CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have shown remarkable progress in photorealistic image synthesis, yet they remain unreliable for generating scenes with a precise number of object instances, particularly in complex and high-density settings. We present CountLoop, a training-free framework that provides diffusion models with accurate instance control through iterative structured feedback. The approach alternates between image generation and multimodal agent evaluation, where a language-guided planner and critic assess object counts, spatial arrangements, and attribute consistency. This feedback is then used to refine layouts and guide subsequent generations. To further improve separation between objects, especially in occluded scenes, we introduce instance-driven attention masking and compositional generation techniques. Experiments on COCO Count, T2I CompBench, and two new high-instance benchmarks show that CountLoop achieves counting accuracy of up to 98% while maintaining spatial fidelity and visual quality, outperforming layout-based and gradient-guided baselines with a score of 0.97.